Quantifying the Economic Response to COVID-19 Mitigations and Death Rates Via Forecasting Purchasing Managers' Indices Using Generalised Network Autoregressive Models with Exogenous Variables
该研究利用广义网络自回归模型，结合贸易网络和COVID-19防控措施及死亡率数据，预测多国采购经理指数，发现该模型比传统向量自回归模型预测误差更小，并模拟了不同干预强度对英国经济的影响。
Abstract Knowledge of the current state of economies, how they respond to COVID-19 mitigations and indicators, and what the future might hold for them is important. We use recently developed generalised network autoregressive (GNAR) models, using trade-determined networks, to model and forecast the Purchasing Managers' Indices for a number of countries. We use networks that link countries where the links themselves, or their weights, are determined by the degree of export trade between the countries. We extend these models to include node-specific time series exogenous variables (GNARX models), using this to incorporate COVID-19 mitigation stringency indices and COVID-19 death rates into our analysis. The highly parsimonious GNAR models considerably outperform vector autoregressive models in terms of mean-squared forecasting error and our GNARX models themselves outperform GNAR ones. Further mixed frequency modelling predicts the extent to which that the UK economy will be affected by harsher, weaker or no interventions.